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<article id="content">
<header>
<h1 class="title">Module <code>Euro-Truck-Simulator-2-Lane-Assist.plugins.UFLDLaneDetection.UFLD.ultrafastLaneDetector.exportLib.ultrafastLaneV2.model_curvelanes</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">import torch
from .backbone import resnet
from .layer import initialize_weights
from .seg_model import SegHead

class parsingNet(torch.nn.Module):
    def __init__(self, pretrained=True, backbone=&#39;50&#39;, num_grid_row = None, num_cls_row = None, num_grid_col = None, num_cls_col = None, 
                num_lane_on_row = None, num_lane_on_col = None, use_aux=False, input_height = None, input_width = None):
        super(parsingNet, self).__init__()
        self.num_grid_row = num_grid_row
        self.num_cls_row = num_cls_row
        self.num_grid_col = num_grid_col
        self.num_cls_col = num_cls_col
        self.num_lane_on_row = num_lane_on_row
        self.num_lane_on_col = num_lane_on_col
        self.use_aux = use_aux

        self.input_height = input_height
        self.input_width = input_width


        self.dim1 = self.num_grid_row * self.num_cls_row
        self.dim2 = 2 * self.num_cls_row
        self.dim3 = self.num_grid_col * self.num_cls_col
        self.dim4 = 2 * self.num_cls_col
        self.total_dim_row = self.dim1 + self.dim2
        self.total_dim_col = self.dim3 + self.dim4
        mlp_mid_dim = 2048
        
        self.input_dim = (self.input_height//32) * (self.input_width//32) * 9

        self.model = resnet(backbone, pretrained=pretrained)

        self.cls_distribute = torch.nn.Sequential(
            torch.nn.Conv2d(512, 128, 3, padding=1),
            torch.nn.ReLU(),
            torch.nn.Conv2d(128, 20, 3, padding=1),
        )
        self.cls = torch.nn.Sequential(
            torch.nn.LayerNorm(self.input_dim),
            torch.nn.Linear(self.input_dim, mlp_mid_dim),
            torch.nn.ReLU()
        )
        self.cls_row = torch.nn.Linear(mlp_mid_dim, self.total_dim_row)
        self.cls_col = torch.nn.Linear(mlp_mid_dim, self.total_dim_col)
        self.pool = torch.nn.Conv2d(512,8,1) if backbone in [&#39;34&#39;,&#39;18&#39;, &#39;34fca&#39;] else torch.nn.Conv2d(2048,8,1)
        if self.use_aux:
            self.seg_head = SegHead(backbone, num_lane_on_row + num_lane_on_col)
        initialize_weights(self.cls_distribute)
        initialize_weights(self.cls)
        initialize_weights([self.cls_row])
        initialize_weights([self.cls_col])

    def forward(self, x):
        x2,x3,fea = self.model(x)
        if self.use_aux:
            seg_out = self.seg_head(x2, x3,fea)
        lane_token = self.cls_distribute(fea).reshape(-1, 20, 1, self.input_height//32, self.input_width//32)
        fea = self.pool(fea).unsqueeze(1).repeat(1, 20, 1, 1, 1)
        fea = torch.cat([fea, lane_token], 2)

        fea = fea.view(-1, self.input_dim)
        out = self.cls(fea).reshape(-1, 20, 2048)
        out_row = self.cls_row(out[:, :10, :]).permute(0, 2, 1)
        out_col = self.cls_col(out[:, 10:, :]).permute(0, 2, 1)

        pred_dict = {&#39;loc_row&#39;: out_row[:,:self.dim1, :].view(-1,self.num_grid_row, self.num_cls_row, self.num_lane_on_row), 
                &#39;loc_col&#39;: out_col[:,:self.dim3, :].view(-1, self.num_grid_col, self.num_cls_col, self.num_lane_on_col),
                &#39;exist_row&#39;: out_row[:,self.dim1:self.dim1+self.dim2, :].view(-1, 2, self.num_cls_row, self.num_lane_on_row), 
                &#39;exist_col&#39;: out_col[:,self.dim3:self.dim3+self.dim4, :].view(-1, 2, self.num_cls_col, self.num_lane_on_col),
                &#39;lane_token_row&#39;: lane_token[:, :10, :, :].sum(1), &#39;lane_token_col&#39;: lane_token[:, 10:, :, :].sum(1)}
        if self.use_aux:
            pred_dict[&#39;seg_out&#39;] = seg_out
        
        return pred_dict

    def forward_tta(self, x):
        raise NotImplementedError

def get_model(cfg):
    return parsingNet(pretrained = True, backbone=cfg.backbone, num_grid_row = cfg.num_cell_row, num_cls_row = cfg.num_row, num_grid_col = cfg.num_cell_col, num_cls_col = cfg.num_col, num_lane_on_row = cfg.num_lanes, num_lane_on_col = cfg.num_lanes, use_aux = cfg.use_aux, input_height = cfg.train_height, input_width = cfg.train_width).cuda()</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="Euro-Truck-Simulator-2-Lane-Assist.plugins.UFLDLaneDetection.UFLD.ultrafastLaneDetector.exportLib.ultrafastLaneV2.model_curvelanes.get_model"><code class="name flex">
<span>def <span class="ident">get_model</span></span>(<span>cfg)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def get_model(cfg):
    return parsingNet(pretrained = True, backbone=cfg.backbone, num_grid_row = cfg.num_cell_row, num_cls_row = cfg.num_row, num_grid_col = cfg.num_cell_col, num_cls_col = cfg.num_col, num_lane_on_row = cfg.num_lanes, num_lane_on_col = cfg.num_lanes, use_aux = cfg.use_aux, input_height = cfg.train_height, input_width = cfg.train_width).cuda()</code></pre>
</details>
</dd>
</dl>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="Euro-Truck-Simulator-2-Lane-Assist.plugins.UFLDLaneDetection.UFLD.ultrafastLaneDetector.exportLib.ultrafastLaneV2.model_curvelanes.parsingNet"><code class="flex name class">
<span>class <span class="ident">parsingNet</span></span>
<span>(</span><span>pretrained=True, backbone='50', num_grid_row=None, num_cls_row=None, num_grid_col=None, num_cls_col=None, num_lane_on_row=None, num_lane_on_col=None, use_aux=False, input_height=None, input_width=None)</span>
</code></dt>
<dd>
<div class="desc"><p>Base class for all neural network modules.</p>
<p>Your models should also subclass this class.</p>
<p>Modules can also contain other Modules, allowing to nest them in
a tree structure. You can assign the submodules as regular attributes::</p>
<pre><code>import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))
</code></pre>
<p>Submodules assigned in this way will be registered, and will have their
parameters converted too when you call :meth:<code>to</code>, etc.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>As per the example above, an <code>__init__()</code> call to the parent class
must be made before assignment on the child.</p>
</div>
<p>:ivar training: Boolean represents whether this module is in training or
evaluation mode.
:vartype training: bool</p>
<p>Initializes internal Module state, shared by both nn.Module and ScriptModule.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class parsingNet(torch.nn.Module):
    def __init__(self, pretrained=True, backbone=&#39;50&#39;, num_grid_row = None, num_cls_row = None, num_grid_col = None, num_cls_col = None, 
                num_lane_on_row = None, num_lane_on_col = None, use_aux=False, input_height = None, input_width = None):
        super(parsingNet, self).__init__()
        self.num_grid_row = num_grid_row
        self.num_cls_row = num_cls_row
        self.num_grid_col = num_grid_col
        self.num_cls_col = num_cls_col
        self.num_lane_on_row = num_lane_on_row
        self.num_lane_on_col = num_lane_on_col
        self.use_aux = use_aux

        self.input_height = input_height
        self.input_width = input_width


        self.dim1 = self.num_grid_row * self.num_cls_row
        self.dim2 = 2 * self.num_cls_row
        self.dim3 = self.num_grid_col * self.num_cls_col
        self.dim4 = 2 * self.num_cls_col
        self.total_dim_row = self.dim1 + self.dim2
        self.total_dim_col = self.dim3 + self.dim4
        mlp_mid_dim = 2048
        
        self.input_dim = (self.input_height//32) * (self.input_width//32) * 9

        self.model = resnet(backbone, pretrained=pretrained)

        self.cls_distribute = torch.nn.Sequential(
            torch.nn.Conv2d(512, 128, 3, padding=1),
            torch.nn.ReLU(),
            torch.nn.Conv2d(128, 20, 3, padding=1),
        )
        self.cls = torch.nn.Sequential(
            torch.nn.LayerNorm(self.input_dim),
            torch.nn.Linear(self.input_dim, mlp_mid_dim),
            torch.nn.ReLU()
        )
        self.cls_row = torch.nn.Linear(mlp_mid_dim, self.total_dim_row)
        self.cls_col = torch.nn.Linear(mlp_mid_dim, self.total_dim_col)
        self.pool = torch.nn.Conv2d(512,8,1) if backbone in [&#39;34&#39;,&#39;18&#39;, &#39;34fca&#39;] else torch.nn.Conv2d(2048,8,1)
        if self.use_aux:
            self.seg_head = SegHead(backbone, num_lane_on_row + num_lane_on_col)
        initialize_weights(self.cls_distribute)
        initialize_weights(self.cls)
        initialize_weights([self.cls_row])
        initialize_weights([self.cls_col])

    def forward(self, x):
        x2,x3,fea = self.model(x)
        if self.use_aux:
            seg_out = self.seg_head(x2, x3,fea)
        lane_token = self.cls_distribute(fea).reshape(-1, 20, 1, self.input_height//32, self.input_width//32)
        fea = self.pool(fea).unsqueeze(1).repeat(1, 20, 1, 1, 1)
        fea = torch.cat([fea, lane_token], 2)

        fea = fea.view(-1, self.input_dim)
        out = self.cls(fea).reshape(-1, 20, 2048)
        out_row = self.cls_row(out[:, :10, :]).permute(0, 2, 1)
        out_col = self.cls_col(out[:, 10:, :]).permute(0, 2, 1)

        pred_dict = {&#39;loc_row&#39;: out_row[:,:self.dim1, :].view(-1,self.num_grid_row, self.num_cls_row, self.num_lane_on_row), 
                &#39;loc_col&#39;: out_col[:,:self.dim3, :].view(-1, self.num_grid_col, self.num_cls_col, self.num_lane_on_col),
                &#39;exist_row&#39;: out_row[:,self.dim1:self.dim1+self.dim2, :].view(-1, 2, self.num_cls_row, self.num_lane_on_row), 
                &#39;exist_col&#39;: out_col[:,self.dim3:self.dim3+self.dim4, :].view(-1, 2, self.num_cls_col, self.num_lane_on_col),
                &#39;lane_token_row&#39;: lane_token[:, :10, :, :].sum(1), &#39;lane_token_col&#39;: lane_token[:, 10:, :, :].sum(1)}
        if self.use_aux:
            pred_dict[&#39;seg_out&#39;] = seg_out
        
        return pred_dict

    def forward_tta(self, x):
        raise NotImplementedError</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li>torch.nn.modules.module.Module</li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="Euro-Truck-Simulator-2-Lane-Assist.plugins.UFLDLaneDetection.UFLD.ultrafastLaneDetector.exportLib.ultrafastLaneV2.model_curvelanes.parsingNet.forward"><code class="name flex">
<span>def <span class="ident">forward</span></span>(<span>self, x) ‑> Callable[..., Any]</span>
</code></dt>
<dd>
<div class="desc"><p>Defines the computation performed at every call.</p>
<p>Should be overridden by all subclasses.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Although the recipe for forward pass needs to be defined within
this function, one should call the :class:<code>Module</code> instance afterwards
instead of this since the former takes care of running the
registered hooks while the latter silently ignores them.</p>
</div></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def forward(self, x):
    x2,x3,fea = self.model(x)
    if self.use_aux:
        seg_out = self.seg_head(x2, x3,fea)
    lane_token = self.cls_distribute(fea).reshape(-1, 20, 1, self.input_height//32, self.input_width//32)
    fea = self.pool(fea).unsqueeze(1).repeat(1, 20, 1, 1, 1)
    fea = torch.cat([fea, lane_token], 2)

    fea = fea.view(-1, self.input_dim)
    out = self.cls(fea).reshape(-1, 20, 2048)
    out_row = self.cls_row(out[:, :10, :]).permute(0, 2, 1)
    out_col = self.cls_col(out[:, 10:, :]).permute(0, 2, 1)

    pred_dict = {&#39;loc_row&#39;: out_row[:,:self.dim1, :].view(-1,self.num_grid_row, self.num_cls_row, self.num_lane_on_row), 
            &#39;loc_col&#39;: out_col[:,:self.dim3, :].view(-1, self.num_grid_col, self.num_cls_col, self.num_lane_on_col),
            &#39;exist_row&#39;: out_row[:,self.dim1:self.dim1+self.dim2, :].view(-1, 2, self.num_cls_row, self.num_lane_on_row), 
            &#39;exist_col&#39;: out_col[:,self.dim3:self.dim3+self.dim4, :].view(-1, 2, self.num_cls_col, self.num_lane_on_col),
            &#39;lane_token_row&#39;: lane_token[:, :10, :, :].sum(1), &#39;lane_token_col&#39;: lane_token[:, 10:, :, :].sum(1)}
    if self.use_aux:
        pred_dict[&#39;seg_out&#39;] = seg_out
    
    return pred_dict</code></pre>
</details>
</dd>
<dt id="Euro-Truck-Simulator-2-Lane-Assist.plugins.UFLDLaneDetection.UFLD.ultrafastLaneDetector.exportLib.ultrafastLaneV2.model_curvelanes.parsingNet.forward_tta"><code class="name flex">
<span>def <span class="ident">forward_tta</span></span>(<span>self, x)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def forward_tta(self, x):
    raise NotImplementedError</code></pre>
</details>
</dd>
</dl>
</dd>
</dl>
</section>
</article>
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<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="Euro-Truck-Simulator-2-Lane-Assist.plugins.UFLDLaneDetection.UFLD.ultrafastLaneDetector.exportLib.ultrafastLaneV2" href="index.html">Euro-Truck-Simulator-2-Lane-Assist.plugins.UFLDLaneDetection.UFLD.ultrafastLaneDetector.exportLib.ultrafastLaneV2</a></code></li>
</ul>
</li>
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="">
<li><code><a title="Euro-Truck-Simulator-2-Lane-Assist.plugins.UFLDLaneDetection.UFLD.ultrafastLaneDetector.exportLib.ultrafastLaneV2.model_curvelanes.get_model" href="#Euro-Truck-Simulator-2-Lane-Assist.plugins.UFLDLaneDetection.UFLD.ultrafastLaneDetector.exportLib.ultrafastLaneV2.model_curvelanes.get_model">get_model</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="Euro-Truck-Simulator-2-Lane-Assist.plugins.UFLDLaneDetection.UFLD.ultrafastLaneDetector.exportLib.ultrafastLaneV2.model_curvelanes.parsingNet" href="#Euro-Truck-Simulator-2-Lane-Assist.plugins.UFLDLaneDetection.UFLD.ultrafastLaneDetector.exportLib.ultrafastLaneV2.model_curvelanes.parsingNet">parsingNet</a></code></h4>
<ul class="">
<li><code><a title="Euro-Truck-Simulator-2-Lane-Assist.plugins.UFLDLaneDetection.UFLD.ultrafastLaneDetector.exportLib.ultrafastLaneV2.model_curvelanes.parsingNet.forward" href="#Euro-Truck-Simulator-2-Lane-Assist.plugins.UFLDLaneDetection.UFLD.ultrafastLaneDetector.exportLib.ultrafastLaneV2.model_curvelanes.parsingNet.forward">forward</a></code></li>
<li><code><a title="Euro-Truck-Simulator-2-Lane-Assist.plugins.UFLDLaneDetection.UFLD.ultrafastLaneDetector.exportLib.ultrafastLaneV2.model_curvelanes.parsingNet.forward_tta" href="#Euro-Truck-Simulator-2-Lane-Assist.plugins.UFLDLaneDetection.UFLD.ultrafastLaneDetector.exportLib.ultrafastLaneV2.model_curvelanes.parsingNet.forward_tta">forward_tta</a></code></li>
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